A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results to produce original error series. Next, to enhance the quality of original error data by reducing the uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic results is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%.

Due to the widespread deployment of distributed energy resources (DERs) and the liberalization of electricity market, traditional distribution networks are undergoing a transition to active distribution systems (ADSs), and the traditional deterministic planning methods have become unsuitable under the high penetration of DERs. Aiming to develop appropriate models and methodologies for the planning of ADSs, the key features of ADS planning problem are analyzed from the different perspectives, such as the allocation of DGs and ESS, coupling of operation and planning, and high-level uncertainties. Based on these analyses, this comprehensive literature review summarizes the latest research and development associated with ADS planning. The planning models and methods proposed in these research works are analyzed and categorized from different perspectives including objectives, decision variables, constraint conditions, and solving algorithms. The key theoretical issues and challenges of ADS planning are extracted and discussed. Meanwhile, emphasis is also given to the suitable suggestions to deal with these abovementioned issues based on the available literature and comparisons between them. Finally, several important research prospects are recommended for further research in ADS planning field, such as planning with multiple micro-grids (MGs), collaborative planning between ADSs and information communication system (ICS), and planning from different perspectives of multi-stakeholders.

The charging load forecasting is of significant importance to the economic operation of charging stations and the stable operation of power systems. The charging stations couple power systems with transportation systems. Their charging loads are not only affected by the driver’s driving and charging behavior simultaneously, but also by the above two networks. There is much literature reporting load forecasting combined with historical charging loads in temporal dimension. However, the spatial data of charging stations in the neighboring areas is helpful for load forecasting, as they share common conditions, including traffic and weather factors. This paper proposes load forecasting of electric vehicle charging stations based on the spatiotemporal multi-graph convolutional networks (STMGCN). STMGCN contains three components: gated dilated causal convolution, spatiotemporal attention mechanism, and multi-graph convolutional layer. Firstly, the load model based on graph structure is established according to the historical load and geographical information of charging stations. Then, a load forecasting method of multiple charging stations is proposed, which can effectively share spatiotemporal relationships among stations. Finally, experiments on real-world dataset illustrate that STMGCN is capable of improving the accuracy of charging stations load forecasting compared with the baselines, which shows the effectiveness of the model.

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.